Another class of ANNs that has recently grown in popularity and utilized in propagation modeling, is the RNN. Contrary to the previously discussed ANN types, these networks exhibit a dynamic behaviour, whereby their output depends not only on the current input, but on previous inputs as well. Hence, RNNs process sequences of input data in a recurrent fashion. Many different variations of RNNs have been used for propagation modeling purposes, such as the echo state networks (ESNs), [1]}, Elman RNNs [2]}, standard RNNs [3]}, [4]} and gated RNNs that use a gated recurrent unit (GRU) instead of a standard recurrent unit cell [5]}, [4]}. The most important and popular type of RNN is the long short-term memory (LSTM) RNN that can capture longer dependencies in the input data, compared to the other types of RNNs [7]}, [8]}, [4]}, [10]}, [11]}. In propagation modeling problems, the input sequence to the RNN is usually spatial [4]} or temporal [5]}, [3]}. The ability to learn temporal sequences also makes RNNs ideal for modeling time variations in a communication channel.
